District Data Labs - Modern Methods for Sentiment Analysis Modern Methods for Sentiment Analysis Michael Czerny Sentiment analysis is a common application of Natural Language Processing (NLP) methodologies, particularly classification, whose goal is to extract the emotional content in text. The simplest form of sentiment analysis is to use a dictionary of good and bad words. Another common method is to treat a text as a “bag of words”. Word2Vec and Doc2Vec Recently, Google developed a method called Word2Vec that captures the context of words, while at the same time reducing the size of the data. Figure 1: Architecture for the CBOW and Skip-gram method, taken from Efficient Estimation of Word Representations in Vector Space. These word vectors now capture the context of surrounding words. However, even with the above method of averaging word vectors, we are ignoring word order. Figure 2: Architecture for Doc2Vec, taken from Distributed Representations of Sentences and Documents. Word2Vec Example in Python "ate" - "eat" + "speak" = "spoke" Conclusion
15 Steps to Implement a Neural Net – code-spot (Original image by Hljod.Huskona / CC BY-SA 2.0). I used to hate neural nets. Mostly, I realise now, because I struggled to implement them correctly. This tutorial is an implementation guide. I tried to make the design as straightforward as possible. To keep the implementation simple, I did not bother with optimisation. The brief introduction below is a very superficial explanation of a neural net; it is included mostly to establish terminology and help you map it to the concepts that are explained in more detail in other texts. Preliminary remarks and overview What we are doing The problem we are trying to solve is this: we have some measurements (features of an object), and we have a good idea that these features might tell us in which class the object belongs. Now in heaven, there is a function for exactly that task – we give the function the features, and it spits out the class. Down here on earth, we are not so lucky (well, not for most problems anyway). Representation Overview 1. 2. 3.
Former Facebook executive says society will COLLAPSE within 30 years as robots put half of humans out of work A former Facebook executive has quit his job and now lives as a recluse in the wilderness - because he is convinced that machines will take over the world. Antonio Garcia Martinez worked as a project manager for the social media giant in Silicon Valley but became terrified by the relentless march of technology. Facebook Getty - Contributor He reckons that machines will have taken half of humanity's jobs within 30 years, sparking revolt and armed conflict. So he quit his job, fled his home and now lives in woodland north of Seattle with a gun for protection. He spoke to new two-part BBC2 documentary "Secrets of Silicon Valley", which explores the growing influence of the tech hub on global development. Mr Martinez said: "If the world really does end, there aren't going to be many places to run. "Within 30 years, half of humanity won't have a job. "I've seen what the world will look like in five to 10 years. This video isn't encoded for your device Getty Images Facebook friend
Understanding Convolutional Neural Networks for NLP | WildML When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated photo tagging to self-driving cars. More recently we’ve also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I’ll try to summarize what CNNs are, and how they’re used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I’ll start there, and then slowly move towards NLP. What is Convolution? The for me easiest way to understand a convolution is by thinking of it as a sliding window function applied to a matrix. Convolution with 3×3 Filter. Imagine that the matrix on the left represents an black and white image. You may be wondering wonder what you can actually do with this. The GIMP manual has a few other examples. Narrow vs. .
The Nature of Code “You can’t process me with a normal brain.” — Charlie Sheen We’re at the end of our story. This is the last official chapter of this book (though I envision additional supplemental material for the website and perhaps new chapters in the future). We began with inanimate objects living in a world of forces and gave those objects desires, autonomy, and the ability to take action according to a system of rules. The human brain can be described as a biological neural network—an interconnected web of neurons transmitting elaborate patterns of electrical signals. Figure 10.1 The good news is that developing engaging animated systems with code does not require scientific rigor or accuracy, as we’ve learned throughout this book. In this chapter, we’ll begin with a conceptual overview of the properties and features of neural networks and build the simplest possible example of one (a network that consists of a single neuron). 10.1 Artificial Neural Networks: Introduction and Application Figure 10.2
How Ideology Is Like Pokemon Go Released in July 2016, Pokémon Go is a location-based, augmented-reality game for mobile devices, typically played on mobile phones; players use the device’s GPS and camera to capture, battle, and train virtual creatures (“Pokémon”) who appear on the screen as if they were in the same real-world location as the player: As players travel the real world, their avatar moves along the game’s map. Different Pokémon species reside in different areas—for example, water-type Pokémon are generally found near water. When a player encounters a Pokémon, AR (Augmented Reality) mode uses the camera and gyroscope on the player’s mobile device to display an image of a Pokémon as though it were in the real world. The first step in this direction of technology imitating ideology was taken a couple of years ago by Pranav Mistry, a member of the Fluid Interfaces Group at the Massachusetts Institute of Technology Media Lab, who developed a wearable “gestural interface” called “SixthSense.” References 1. 2.
Understanding Natural Language with Deep Neural Networks Using Torch This post was co-written by Soumith Chintala and Wojciech Zaremba of Facebook AI Research. Language is the medium of human communication. Giving machines the ability to learn and understand language enables products and possibilities that are not imaginable today. One can understand language at varying granularities. As an example, language understanding gives one the ability to understand that the sentences “I’m on my way home.” and “I’m driving back home.” both convey that the speaker is going home. Word Maps and Language Models For a machine to understand language, it first has to develop a mental map of words, their meanings and interactions with other words. Word embeddings can either be learned in a general-purpose fashion before-hand by reading large amounts of text (like Wikipedia), or specially learned for a particular task (like sentiment analysis). An even simpler metric is to predict the next word in the sentence. “I am eating _____” “I am eating an apple.” Learn More at GTC 2015
Basic Neural Network Tutorial : C++ Implementation and Source Code | Taking Initiative So I’ve now finished the first version of my second neural network tutorial covering the implementation and training of a neural network. I noticed mistakes and better ways of phrasing things in the first tutorial (thanks for the comments guys) and rewrote large sections. This will probably occur with this tutorial in the coming week so please bear with me. I’m pretty overloaded with work and assignments so I haven’t been able to dedicate as much time as I would have liked to this tutorial, even so I feel its rather complete and any gaps will be filled in by my source code. Introduction & Implementation Okay so how do we implement our neural network? Our neuron valuesOur weightsOur weight changesOur error gradients Now I’ve seen various implementations and wait for it… here comes an OO rant: I don’t understand why people feel the need to encapsulate everything in classes. The other common approach is to model each layer as an object? Initialization of Neural Network The Training Data Sets
Take it from the insiders: Silicon Valley is eating your soul | John Harris One source of angst came close to being 2017’s signature subject: how the internet and the tiny handful of companies that dominate it are affecting both individual minds and the present and future of the planet. The old idea of the online world as a burgeoning utopia looks to have peaked around the time of the Arab spring, and is in retreat. If you want a sense of how much has changed, picture the president of the US tweeting his latest provocation in the small hours, and consider an array of words and phrases now freighted with meaning: Russia, bots, troll farms, online abuse, fake news, dark money. Another sign of how much things have shifted is a volte-face by Silicon Valley’s most powerful man. The company has reached a fascinating point in its evolution; it is as replete with importance and interest as any political party. Then there is Tristan Harris, a former high-up at Google who is now hailed as “the closest thing Silicon Valley has to a conscience”. Good for him.
NaturalNode/natural Unsupervised Feature Learning and Deep Learning Tutorial Problem Formulation As a refresher, we will start by learning how to implement linear regression. The main idea is to get familiar with objective functions, computing their gradients and optimizing the objectives over a set of parameters. These basic tools will form the basis for more sophisticated algorithms later. Readers that want additional details may refer to the Lecture Note on Supervised Learning for more. Our goal in linear regression is to predict a target value starting from a vector of input values . Our goal is to find a function so that we have for each training example. To find a function where we must first decide how to represent the function . This function is the “cost function” for our problem which measures how much error is incurred in predicting for a particular choice of . Function Minimization We now want to find the choice of that minimizes as given above. Differentiating the cost function as given above with respect to a particular parameter gives us:
The Case for Responsible Innovation | DigitalNext Is technological innovation good or bad? Seems like a silly question on the surface. But we have questions: Can self-driving cars ever be safe? We have concerns: Fake news, fake ads, fake accounts, bots, foreign governments interfering with our elections … The courts have historically decided that technology is neither intrinsically good nor bad, but they have expressed the opinion that people must be responsible and held accountable for how it is used. It's illegal to text and drive. Fear, uncertainty and doubt in Las Vegas Once a beacon of optimism, the tech industry has come under pressure as concerns mount about potential negative impacts of innovation. My colleagues at PwC and I agree that the time has come to seriously consider a responsible approach to innovation. At CES in Las Vegas next week, we'll present a discussion that explores the three basic approaches to the problem of regulating technological innovation: 1. 2. 3. Our thinking
unrealengine Jan 3, 2018 Unreal Engine 4 Mastery: Create Multiplayer Games with C++ in New Course from Udemy By Daniel Kayser Unleash the power of C++ and Blueprint to develop Multiplayer Games with AI... Jan 1, 2018 Getting Started with Unreal Multiplayer in C++ By Sam Pattuzzi While Unreal Engine offers fantastic multiplayer support right out of the b... Dec 30, 2017 Unreal Engine Developers Featured in IndieDB’s Top 100 of 2017 By Jess Hider As the year comes to a close, it’s a perfect time to look back on some of t...